The proposed research addresses the processes underlying face recognition using computational models applied to 'face-space' representations. Adopted from the categorization literature, face-space representation are derived from multi-dimensional scaling (MDS) analysis of similarity ratings between all pairs of faces. The resulting solutions reveal the dimensions along which faces differ. A particular face is described as a point in this space, which defines its similarity to all other faces. In the current experiments, the face-space representation is used to account for a variety of phenomenon, including cross-racial identification, old/new and force-choice recognition, and confidence ratings. These latter ratings are particularly important for eyewitness testimony scenarios, and the computational models that account for recognition performance and confidence can be used to demonstrate why confidence and accuracy are poorly correlated. The data are analyzed using equivalence techniques that allow conclusions about the relation between dependent variables, as well as computational models that describe the relation between a particular face's location in face space and recognition performance. The current modeling attempts to incorporate aspects of familiarity and recollective mechanisms, as well as a process known as subjective memorability in which participants make conclusions about a particular face on the basis of their judgement of whether it would have been remembered. Together the experiments address questions about the representation of faces in memory, and how these presentations procedure overt responses such as a recognition decision or a confidence rating. The conclusions are readily applied to legal situations in which an eyewitness is asked to chose among several similar alternatives and express a feeling of confidence. The cross-racial identification experiments provide a better understanding of the role of experience in shaping our perception of faces of other races.